AI has the potential to underpin so much of the manufacturing process and drive the industry 4.0 transformational change across the sector.

As McKinsey noted by looking at just one form of AI (generative AI): “Gen AI can unlock untapped productivity during production, leveraging root cause analysis to predict failures and reduce defects, and draft easy-to-follow dynamic work instructions. It can also augment operator stations by offering live, AI-supported troubleshooting and operating guidelines.”

Generative AI should be of particular interest to the manufacturing sector. For just one example of how transformative it could be, as IBM noted AI will significantly help to make the digital twin promise real, which will have a major role in supporting manufacturing into the future.

And yet, research shows that manufacturing executives are struggling to fully embrace the AI opportunity. As a global study by Forrester Consulting found, manufacturing executives are bullish on AI, with 88% acknowledging that it can enhance product quality and 86% saying it will improve time-to-market, but almost three quarters (71%) of business leaders are concerned with a lack of synergy between design and manufacturing teams, which is inhibiting AI adoption. Furthermore, nearly half (42%) are concerned about ineffective sharing of data and insights. 

As anyone who has ever been involved in AI knows, poor data practices are the quickest way to undermine any efforts to adopt an AI strategy. 

For Australian manufacturers, this is a particular concern. An AFR report from earlier in the year highlights that Australian leaders were “falling behind” in finding new ways to generate revenue. 

Transformation, Digitalisation and AI are considered key to being able to develop those revenue streams, and yet, according to the AFR article, 56% of executives cite a lack of tech capabilities in the workforce as a major inhibitor to change. 

Without tech capabilities and alignment across the organisation, any efforts to drive an AI strategy are doomed to failure. 

So, What Can Australian Manufacturers Do To Embrace AI?

To harness the full potential of AI, Australian manufacturers need to make forward momentum in several key areas. 

Modernising Legacy Systems

One of the most critical steps is to overcome legacy hardware and IT environments. Legacy systems are often inflexible and cannot easily integrate with new technologies and can effectively render the integration and scaling of AI technologies impossible. 

To move forward and overcome the legacy environment, manufacturers should consider the following:

• Assessment and Planning: Conduct a thorough assessment of current hardware and IT systems to identify bottlenecks and areas for improvement.
• Investment in Scalable Solutions: Invest in scalable cloud-based platforms that can grow with the business and accommodate AI applications.
• Partnerships with Organisations Like Wild Tech: Form strategic partnerships with technology providers who can offer expertise and solutions tailored to manufacturing needs.

Cultivating a Tech-Savvy Workforce

As business leaders regularly acknowledge, in Australia there is a significant challenge in the current lack of tech capabilities within the workforce. This is something that can be overcome without a manufacturer needing to hire a large IT team, however. To address this challenge, organisations should focus on:

• Upskilling Existing Employees: Implement training programs to upskill existing employees in AI and related technologies.
• Attracting AI Talent In Non-IT Roles: Attract new talent with the necessary broad base of skills by offering competitive packages and a clear career progression path.
• Fostering a Culture of Innovation: Encourage a culture of continuous learning and innovation within the organisation – employees are motivated more by opportunity and career development than ever, and a “good employer” will attract the best employees from a limited pool.

Streamlining Data Practices

One of the key reasons for overcoming legacy IT issues is to enable the manufacturer to do more with data. Effective data sharing and management are crucial for AI adoption. Manufacturers must:

• Improve Data Collection: Ensure that data collection methods are efficient and provide high-quality data.
• Enhance Data Accessibility: Make data easily accessible to all relevant teams to facilitate collaboration and insight sharing.
• Implement Data Governance: Establish clear data governance policies to maintain data integrity and security.

The pathway to AI is not straightforward, especially for manufacturers that have legacy IT systems and lower levels of investment into technology innovation. However, by addressing these key areas, Australian manufacturers can position themselves to be right at the forefront of the Industry 4.0 revolution. The journey towards AI adoption is a strategic one that requires commitment, investment, and a willingness to evolve. With the right approach, manufacturers can unlock new levels of efficiency, innovation, and growth.